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The inverse of material properties of functionally graded pipes using the dispersion of guided waves and an artificial neural network

机译:利用导波色散和人工神经网络对功能梯度管道的材料特性进行反演

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摘要

Using guided circumferential wave dispersion characteristics, an inverse method based on artificial neural network (ANN) is presented to determine the material properties of functionally graded material (FGM) pipes. The group velocities of lowest modes at six lower frequencies are used as the inputs of the ANN model. The distribution function of the volume fraction of the FGM pipe is fitted to a polynomial, then the outputs of the ANN are the coefficients of the fitting polynomial. The Legendre polynomial method is employed as the forward solver to calculate the dispersion curves for the FGM pipe. Levenberg-Marquardt algorithm is used as numerical optimization to speed up the training process of the ANN model.
机译:利用导向的圆周波频散特性,提出了一种基于人工神经网络的逆方法来确定功能梯度材料(FGM)管道的材料性能。六个较低频率处的最低模式的组速度用作ANN模型的输入。将FGM管道的体积分数的分布函数拟合到多项式,然后ANN的输出就是拟合多项式的系数。勒让德多项式方法被用作前向求解器来计算FGM管道的色散曲线。 Levenberg-Marquardt算法被用作数值优化,以加快ANN模型的训练过程。

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